The authors investigate the application of deep convolutional neural networks (CNNs) to the problem of radiometric identification, i.e. the task of authenticating wireless devices on the basis of their radio frequency (RF) emissions, which contain features directly related to the physical properties of the wireless devices. They collected digitised RF from 12 wireless devices, and used various techniques to transform the time series derived from the RF to images. A deep CNN is then applied to the images. The authors' results show that the identification performance of the combination of deep CNN with an image representation significantly outperforms conventional methods based on dissimilarity on the original time series. Moreover, a specific comparison among RF‐to‐image techniques show that on their datasets the wavelet‐based approach outperforms other approaches, also in the presence of white Gaussian noise.

The correct identification of smartphones has various applications in the field of security or the fight against counterfeiting. As the level of sophistication in counterfeit electronics increases, detection procedures must become more accurate but also not destructive for the smartphone under testing. Some components of the smartphone are more likely to reveal their authenticity even without a physical inspection, since they are characterized by hardware fingerprints detectable by simply examining the data they provide. This is the case of MEMS (Micro Electro-Mechanical Systems) components like accelerometers and gyroscopes, where tiny differences and imprecisions in the manufacturing process determine unique patterns in the data output. In this paper, we present the experimental evaluation of the identification of smartphones through their built-in MEMS components. In our study, three different phones of the same model are subject to repeatable movements (composing a repeatable scenario) using an high precision robotic arm. The measurements from MEMS for each repeatable scenario are collected and analyzed. The identification algorithm is based on the extraction of the statistical features of the collected data for each scenario. The features are used in a support vector machine (SVM) classifier to identify the smartphone. The results of the evaluation are presented for different combinations of features and Inertial Measurement Unit (IMU) outputs, which show that detection accuracy of higher than 90% is achievable.

In this letter, we apply a Bernoulli Filter for moving target detection and tracking using real Multiple Input Multiple Output radar data in a challenging environment. The interest in MIMO radars has been growing since they can provide high performance at lower cost than conventional antenna arrays. The Bernoulli Filter performs tracking using the Track-Before-Detect approach, which is required to achieve satisfactory tracking performance in low signal-to-noise scenarios. The algorithm is validated with real MIMO data of a walking person.

The detection and identification of road anomalies and obstacles in the road infrastructure has been investigated by the research community using different types of sensors. This paper evaluates the detection and identification of road anomalies/obstacles using the data collected from the Inertial Measurement Unit (IMU) installed in a vehicle and in particular from the data generated by the accelerometers’ and gyroscopes’ components. Inspired by the successes of the application of deep learning to various identification problems, this paper investigates the application of Convolutional Neural Network (CNN) to this specific problem. In particular, we propose a novel approach in this context where the time-frequency representation (i.e., spectrogram) is used as an input to the CNN rather than the original time domain data. This approach is evaluated on an experimental dataset collected using 12 different vehicles driving for more than 40 km of road. The results show that the proposed approach outperforms significantly and across different sampling rates both the application of CNN to the original time domain representation and the application of shallow machine learning algorithms. The approach achieves an identification accuracy of 97.2%. The results presented in this paper are based on an extensive optimization both of the CNN algorithm and the spectrogram implementation in terms of window size, type of window, and overlapping ratio. The accurate detection of road anomalies/obstacles could be useful to road infrastructure managers to monitor the quality of the road surface and to improve the accurate positioning of autonomous vehicles because road anomalies/obstacles could be used as landmarks.

Vehicle automation and cooperation is progressively being introduced in traffic networks. As a consequence research to assess its impacts is currently on-going. Adaptive Cruise Control (ACC) is one of the first automated functionalities available for privately owned vehicles. An experimental study has been conducted to investigate the key features of the ACC controller using Global Navigation Satellite System data. The first remarks based on the data focus on the controller's reaction time and desired time gap. Both parameters are essential in order to assess the influence of these technologies to safety and traffic flow. It is a common assumption that autonomous vehicles will have negligible reaction time and desired time gap comparable to that of a human driver. This paper presents an experimental study of an ACC-enabled vehicle on car following mode and a methodology for the estimation of the controller's reaction time that can be used as benchmark in other scenarios. The results show the reaction time to be around l.ls and the time gap to be distinctly larger than that of a human driver. This poses concern on the impact of ACC on traffic flow when a significant number of vehicles will have such systems operating on-board.